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Charlet J, Cui L. Knowledge Representation and Management 2022: Findings in Ontology Development and Applications. Yearb Med Inform 2023; 32:225-229. [PMID: 38147864 PMCID: PMC10751114 DOI: 10.1055/s-0043-1768747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2023] Open
Abstract
OBJECTIVES To select, present, and summarize the best papers in 2022 for the Knowledge Representation and Management (KRM) section of the International Medical Informatics Association (IMIA) Yearbook. METHODS We conducted PubMed queries and followed the IMIA Yearbook guidelines for performing biomedical informatics literature review to select the best papers in KRM published in 2022. RESULTS We retrieved 1,847 publications from PubMed. We nominated 15 candidate best papers, and two of them were finally selected as the best papers in the KRM section. The topics covered by the candidate papers include ontology and knowledge graph creation, ontology applications, ontology quality assurance, ontology mapping standard, and conceptual model. CONCLUSIONS In the KRM best paper selection for 2022, the candidate best papers encompassed a broad range of topics, with ontology and knowledge graph creation remaining a considerable research focus.
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Affiliation(s)
- Jean Charlet
- Sorbonne Université, INSERM, Univ Sorbonne Paris Nord, LIMICS, Paris, France
- AP-HP, DRCI, Paris, France
| | - Licong Cui
- McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
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Cui L, Dhombres F, Charlet J. Knowledge Representation and Management: Notable Contributions in 2021. Yearb Med Inform 2022; 31:236-240. [PMID: 36463882 PMCID: PMC9719756 DOI: 10.1055/s-0042-1742523] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022] Open
Abstract
OBJECTIVES To select, present, and summarize the best papers in the field of Knowledge Representation and Management (KRM) published in 2021. METHODS Following the International Medical Informatics Association (IMIA) Yearbook guidelines, a comprehensive and standardized review of the biomedical informatics literature was performed to select the best KRM papers published in 2021, based on PubMed queries. RESULTS A total of 1,231 publications were retrieved from PubMed. We nominated 15 candidate best papers, and four of them were finally selected as the best papers in the KRM section. The topics covered by these papers include knowledge graph, ontology development, ontology alignment, and the International Classification of Diseases. CONCLUSION In the KRM best paper selection for 2021, the candidate best papers covered a wider spectrum of topics compared to the last year's significant focus on ontology curation. In particular, ontology development for specific domains (e.g., Alzheimer's disease, infectious diseases, bioethics) has received the most attention.
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Affiliation(s)
- Licong Cui
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA,Correspondence to: Licong Cui School of Biomedical Informatics, The University of Texas Health Science Center at Houston7000 Fannin Street Houston, TX 77030USA
| | - Ferdinand Dhombres
- Sorbonne Université, INSERM, Univ Sorbonne Paris Nord, LIMICS, Paris, France,Sorbonne Université, Service de Médecine Foetale, DMU Origyne, AP-HP, Hôpital Armand Trousseau, Paris, France
| | - Jean Charlet
- Sorbonne Université, INSERM, Univ Sorbonne Paris Nord, LIMICS, Paris, France,AP-HP, DRCI, Paris, France
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Liu S, Zhang YL, Zhang LY, Zhao GJ, Lu ZQ. FCGR2C: An emerging immune gene for predicting sepsis outcome. Front Immunol 2022; 13:1028785. [PMID: 36532072 PMCID: PMC9757160 DOI: 10.3389/fimmu.2022.1028785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 11/17/2022] [Indexed: 12/05/2022] Open
Abstract
Background Sepsis is a life-threatening disease associated with immunosuppression. Immunosuppression could ultimately increase sepsis mortality. This study aimed to identify the prognostic biomarkers related to immunity in sepsis. Methods Public datasets of sepsis downloaded from the Gene Expression Omnibus (GEO) database were divided into the discovery cohort and the first validation cohort. We used R software to screen differentially expressed genes (DEGs) and analyzed DEGs' functional enrichment in the discovery dataset. Immune-related genes (IRGs) were filtered from the GeneCards website. A Lasso regression model was used to screen candidate prognostic genes from the intersection of DEGs and IRGs. Then, the candidate prognostic genes with significant differences were identified as prognostic genes in the first validation cohort. We further validated the expression of the prognostic genes in the second validation cohort of 81 septic patients recruited from our hospital. In addition, we used four immune infiltration methods (MCP-counter, ssGSEA, ImmuCellAI, and CIBERSORT) to analyze immune cell composition in sepsis. We also explored the correlation between the prognostic biomarker and immune cells. Results First, 140 genes were identified as prognostic-related immune genes from the intersection of DEGs and IRGs. We screened 18 candidate prognostic genes in the discovery cohort with the lasso regression model. Second, in the first validation cohort, we identified 4 genes (CFHR2, FCGR2C, GFI1, and TICAM1) as prognostic immune genes. Subsequently, we found that FCGR2C was the only gene differentially expressed between survivors and non-survivors in 81 septic patients. In the discovery and first validation cohorts, the AUC values of FCGR2C were 0.73 and 0.67, respectively. FCGR2C (AUC=0.84) had more value than SOFA (AUC=0.80) and APACHE II (AUC=0.69) in evaluating the prognosis of septic patients in our recruitment cohort. Moreover, FCGR2C may be closely related to many immune cells and functions, such as B cells, NK cells, neutrophils, cytolytic activity, and inflammatory promotion. Finally, enrichment analysis showed that FCGR2C was enriched in the phagosome signaling pathway. Conclusion FCGR2C could be an immune biomarker associated with prognosis, which may be a new direction of immunotherapy to reduce sepsis mortality.
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Affiliation(s)
- Si Liu
- Emergency Department, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China,Special Medical Department, Nanchong Central Hospital, Nanchong, Sichuan, China
| | - Yao Lu Zhang
- Emergency Department, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Lu Yao Zhang
- Emergency Department, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Guang Ju Zhao
- Emergency Department, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China,*Correspondence: Guang Ju Zhao, ; Zhong Qiu Lu,
| | - Zhong Qiu Lu
- Emergency Department, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China,*Correspondence: Guang Ju Zhao, ; Zhong Qiu Lu,
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Zha Y, Chong H, Yang P, Ning K. Microbial Dark Matter: from Discovery to Applications. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022; 20:867-881. [PMID: 35477055 PMCID: PMC10025686 DOI: 10.1016/j.gpb.2022.02.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 09/28/2021] [Accepted: 03/22/2022] [Indexed: 01/12/2023]
Abstract
With the rapid increase of the microbiome samples and sequencing data, more and more knowledge about microbial communities has been gained. However, there is still much more to learn about microbial communities, including billions of novel species and genes, as well as countless spatiotemporal dynamic patterns within the microbial communities, which together form the microbial dark matter. In this work, we summarized the dark matter in microbiome research and reviewed current data mining methods, especially artificial intelligence (AI) methods, for different types of knowledge discovery from microbial dark matter. We also provided case studies on using AI methods for microbiome data mining and knowledge discovery. In summary, we view microbial dark matter not as a problem to be solved but as an opportunity for AI methods to explore, with the goal of advancing our understanding of microbial communities, as well as developing better solutions to global concerns about human health and the environment.
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Affiliation(s)
- Yuguo Zha
- MOE Key Laboratory of Molecular Biophysics, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Hui Chong
- MOE Key Laboratory of Molecular Biophysics, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Pengshuo Yang
- MOE Key Laboratory of Molecular Biophysics, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Kang Ning
- MOE Key Laboratory of Molecular Biophysics, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
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Dhombres F, Bonnard J, Bailly K, Maurice P, Papageorghiou A, Jouannic JM. Contributions of artificial intelligence reported in Obstetrics and Gynecology journals: a systematic review. J Med Internet Res 2022; 24:e35465. [PMID: 35297766 PMCID: PMC9069308 DOI: 10.2196/35465] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 02/11/2022] [Accepted: 03/15/2022] [Indexed: 11/13/2022] Open
Abstract
Background The applications of artificial intelligence (AI) processes have grown significantly in all medical disciplines during the last decades. Two main types of AI have been applied in medicine: symbolic AI (eg, knowledge base and ontologies) and nonsymbolic AI (eg, machine learning and artificial neural networks). Consequently, AI has also been applied across most obstetrics and gynecology (OB/GYN) domains, including general obstetrics, gynecology surgery, fetal ultrasound, and assisted reproductive medicine, among others. Objective The aim of this study was to provide a systematic review to establish the actual contributions of AI reported in OB/GYN discipline journals. Methods The PubMed database was searched for citations indexed with “artificial intelligence” and at least one of the following medical subject heading (MeSH) terms between January 1, 2000, and April 30, 2020: “obstetrics”; “gynecology”; “reproductive techniques, assisted”; or “pregnancy.” All publications in OB/GYN core disciplines journals were considered. The selection of journals was based on disciplines defined in Web of Science. The publications were excluded if no AI process was used in the study. Review, editorial, and commentary articles were also excluded. The study analysis comprised (1) classification of publications into OB/GYN domains, (2) description of AI methods, (3) description of AI algorithms, (4) description of data sets, (5) description of AI contributions, and (6) description of the validation of the AI process. Results The PubMed search retrieved 579 citations and 66 publications met the selection criteria. All OB/GYN subdomains were covered: obstetrics (41%, 27/66), gynecology (3%, 2/66), assisted reproductive medicine (33%, 22/66), early pregnancy (2%, 1/66), and fetal medicine (21%, 14/66). Both machine learning methods (39/66) and knowledge base methods (25/66) were represented. Machine learning used imaging, numerical, and clinical data sets. Knowledge base methods used mostly omics data sets. The actual contributions of AI were method/algorithm development (53%, 35/66), hypothesis generation (42%, 28/66), or software development (3%, 2/66). Validation was performed on one data set (86%, 57/66) and no external validation was reported. We observed a general rising trend in publications related to AI in OB/GYN over the last two decades. Most of these publications (82%, 54/66) remain out of the scope of the usual OB/GYN journals. Conclusions In OB/GYN discipline journals, mostly preliminary work (eg, proof-of-concept algorithm or method) in AI applied to this discipline is reported and clinical validation remains an unmet prerequisite. Improvement driven by new AI research guidelines is expected. However, these guidelines are covering only a part of AI approaches (nonsymbolic) reported in this review; hence, updates need to be considered.
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Affiliation(s)
- Ferdinand Dhombres
- Sorbonne University, Armand Trousseau University hospital, Fetal Medicine department, APHP, Armand Trousseau University hospital, Fetal Medicine department, APHP26 AV du Dr Arnold Netter, Paris, FR.,INSERM, Laboratory in Medical Informatics and Knowledge Engineering in e-Health (LIMICS), Paris, FR
| | - Jules Bonnard
- Sorbonne University, Institute for Intelligent Systems and Robotics (ISIR), Paris, FR
| | - Kévin Bailly
- Sorbonne University, Institute for Intelligent Systems and Robotics (ISIR), Paris, FR
| | - Paul Maurice
- Sorbonne University, Armand Trousseau University hospital, Fetal Medicine department, APHP, Paris, FR
| | - Aris Papageorghiou
- Oxford Maternal & Perinatal Health Institute, Green Templeton College, Oxford, GB
| | - Jean-Marie Jouannic
- Sorbonne University, Armand Trousseau University hospital, Fetal Medicine department, APHP, Paris, FR.,INSERM, Laboratory in Medical Informatics and Knowledge Engineering in e-Health (LIMICS), Paris, FR
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Filice RW, Kahn CE. Biomedical Ontologies to Guide AI Development in Radiology. J Digit Imaging 2021; 34:1331-1341. [PMID: 34724143 PMCID: PMC8669056 DOI: 10.1007/s10278-021-00527-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 04/27/2021] [Accepted: 10/13/2021] [Indexed: 10/25/2022] Open
Abstract
The advent of deep learning has engendered renewed and rapidly growing interest in artificial intelligence (AI) in radiology to analyze images, manipulate textual reports, and plan interventions. Applications of deep learning and other AI approaches must be guided by sound medical knowledge to assure that they are developed successfully and that they address important problems in biomedical research or patient care. To date, AI has been applied to a limited number of real-world radiology applications. As AI systems become more pervasive and are applied more broadly, they will benefit from medical knowledge on a larger scale, such as that available through computer-based approaches. A key approach to represent computer-based knowledge in a particular domain is an ontology. As defined in informatics, an ontology defines a domain's terms through their relationships with other terms in the ontology. Those relationships, then, define the terms' semantics, or "meaning." Biomedical ontologies commonly define the relationships between terms and more general terms, and can express causal, part-whole, and anatomic relationships. Ontologies express knowledge in a form that is both human-readable and machine-computable. Some ontologies, such as RSNA's RadLex radiology lexicon, have been applied to applications in clinical practice and research, and may be familiar to many radiologists. This article describes how ontologies can support research and guide emerging applications of AI in radiology, including natural language processing, image-based machine learning, radiomics, and planning.
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Affiliation(s)
- Ross W Filice
- Department of Radiology, MedStar Georgetown University Hospital, Washington, DC, USA
| | - Charles E Kahn
- Department of Radiology and Institute for Biomedical Informatics, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA.
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Dhombres F, Charlet J. Knowledge Representation and Management: Interest in New Solutions for Ontology Curation. Yearb Med Inform 2021; 30:185-190. [PMID: 34479390 PMCID: PMC8416227 DOI: 10.1055/s-0041-1726508] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
Objective:
To select, present and summarize some of the best papers in the field of Knowledge Representation and Management (KRM) published in 2020.
Methods:
A comprehensive and standardized review of the medical informatics literature was performed to select the most interesting papers of KRM published in 2020, based on PubMed queries. This review was conducted according to the IMIA Yearbook guidelines.
Results:
Four best papers were selected among 1,175 publications. In contrast with the papers selected last year, the four best papers of 2020 demonstrated a significant focus on methods and tools for ontology curation and design. The usual KRM application domains (bioinformatics, machine learning, and electronic health records) were also represented.
Conclusion:
In 2020, ontology curation emerges as a significant topic of research interest. Bioinformatics, machine learning, and electronics health records remain significant research areas in the KRM community with various applications. Knowledge representations are key to advance machine learning by providing context and to develop novel bioinformatics metrics. As in 2019, representations serve a great variety of applications across many medical domains, with actionable results and now with growing adhesion to the open science initiative.
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Affiliation(s)
- Ferdinand Dhombres
- Sorbonne Université, INSERM, Univ Sorbonne Paris Nord, LIMICS, Paris, France.,Sorbonne Université, Service de Médecine Fœtale, DMU Origyne, AP-HP, Hôpital Armand Trousseau, Paris, France
| | - Jean Charlet
- Sorbonne Université, INSERM, Univ Sorbonne Paris Nord, LIMICS, Paris, France.,AP-HP, DRCI, Paris, France
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Dhombres F, Charlet J. Design and Use of Semantic Resources: Findings from the Section on Knowledge Representation and Management of the 2020 International Medical Informatics Association Yearbook. Yearb Med Inform 2020; 29:163-168. [PMID: 32823311 PMCID: PMC7442529 DOI: 10.1055/s-0040-1702010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
OBJECTIVE To select, present, and summarize the best papers in the field of Knowledge Representation and Management (KRM) published in 2019. METHODS A comprehensive and standardized review of the biomedical informatics literature was performed to select the most interesting papers of KRM published in 2019, based on PubMed and ISI Web Of Knowledge queries. RESULTS Four best papers were selected among 1,189 publications retrieved, following the usual International Medical Informatics Association Yearbook reviewing process. In 2019, research areas covered by pre-selected papers were represented by the design of semantic resources (methods, visualization, curation) and the application of semantic representations for the integration/enrichment of biomedical data. Besides new ontologies and sound methodological guidance to rethink knowledge bases design, we observed large scale applications, promising results for phenotypes characterization, semantic-aware machine learning solutions for biomedical data analysis, and semantic provenance information representations for scientific reproducibility evaluation. CONCLUSION In the KRM selection for 2019, research on knowledge representation demonstrated significant contributions both in the design and in the application of semantic resources. Semantic representations serve a great variety of applications across many medical domains, with actionable results.
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Affiliation(s)
- Ferdinand Dhombres
- Sorbonne Université, Université Paris Nord, INSERM, UMR_S 1142, LIMICS, Paris, France
- Médecine Sorbonne Université, Service de Médecine Fœtale, Hôpital Armand Trousseau, Paris, France
| | - Jean Charlet
- Sorbonne Université, Université Paris Nord, INSERM, UMR_S 1142, LIMICS, Paris, France
- AP-HP, DRCI, Paris, France
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